![](https://csdnimg.cn/release/download_crawler_static/10801449/bg14.jpg)
Hopper, E., Turton, B.C.H., 2000. An empirical investigation of meta-heuristic and heuristic
algorithm for a 2D packing problem. Eur. J. Oper. Res. 128 (1), 3457.
Karaboga, D., 2005. An Idea Based on Honey Bee Swarm for Numerical Optimization,
Technical Report TR06. Erciyes University, Turkey.
Keller, E.F., 2009. Organisms, machines, and thunderstorms: a history of self-organiza-
tion, part two. Complexity, emergence, and stable attractors. Hist. Stud. Nat. Sci. 39,
131.
Kennedy, J., Eberhart, R.C., 1995. Particle swarm optimisation. In: Proceedings of the IEEE
International Conference on Neural Networks. Piscataway, NJ, pp. 19421948.
Kennedy, J., Eberhart, R.C., Shi, Y., 2001. Swarm Intelligence, Morgan Kaufmann Publishers,
San Diego, USA.
Koziel, S., Yang, X.S., 2011. Computational Optimization, Methods and Algorithms.
Springer, Germany.
Nakrani, S., Tovey, C., 2004. On honey bees and dynamic server allocation in internet host-
ing centers. Adapt. Behav. 12 (34), 223240.
Parpinelli, R.S., Lopes, H.S., 2011. New inspirations in swarm intelligence: a survey. Int. J.
Bio-Inspired Comput. 3, 116.
Pavlyukevich, I., 2007. Le
´
vy flights, non-local search and simulated annealing. J. Comput.
Phys. 226, 18301844.
Pham, D.T., Ghanbarzadeh, A., Koc, E., Otri, S., Rahim, S., Zaidi, M., 2006. The bees algo-
rithm: a novel tool for complex optimisation problems. In: Proceedings of the IPROMS
2006 Conference, pp. 454461.
Prigogine, I., Nicolois, G., 1967. On symmetry-breaking instabilities in dissipative systems.
J. Chem. Phys. 46, 35423550.
Rui, T., Fong, S., Yang, X.S., Deb, S., 2012. Wolf search algorithm with ephemeral memory.
In: Fong, S., Pichappan, P., Mohammed, S., Hung, P., Asghar, S. (Eds.), Proceedings of
the Seventh International Conference on Digital Information Management (ICDIM2012),
(August 2224), Macau, pp. 165172.
Sayadi, M.K., Ramezanian, R., Ghaffari-Nasab, N., 2010. A discrete firefly meta-heuristic
with local search for makespan minimization in permutation flow shop scheduling pro-
blems. Int. J. Ind. Eng. Comput. 1, 110.
Talbi, E.G., 2009. Metaheuristics: From Design to Implementation. John Wiley & Sons,
New Jersey.
Villalobos-Arias, M., Coello Coello, C.A., Herna
´
ndez-Lerma, O., 2005. Asymptotic
convergence of metaheuristics for multiobjective optimization problems. Soft Comput.
10, 10011005.
Wolpert, D.H., Macready, W.G., 1997. No free lunch theorems for optimization. IEEE
Trans. Evol. Comput. 1, 6782.
Yang, X.S., 2005. Engineering optimization via nature-inspired virtual bee algorithms,
Artificial Intelligence and Knowledge Engineering Applications: A Bioinspired
Approach, Springer, Berlin/Heidelberg, (Lecture Notes in Computer Science, vol. 3562,
pp. 317323).
Yang, X.S., 2008. Nature-Inspired Metaheuristic Algorithms, first ed. Luniver Press, UK.
Yang, X.S., 2009. Firefly algorithms for multimodal optimisation. In: Watanabe, O.,
Zeugmann, T. (Eds.), Fifth Symposium on Stochastic Algorithms, Foundation and
Applications (SAGA 2009), LNCS, vol. 5792, pp. 169178.
Yang, X.S., 2010a. Nature-Inspired Metaheuristic Algorithms. second ed. Luniver Press, UK.
Yang, X.S., 2010b. Engineering Optimization: An Introduction with Metaheuristic
Applications. John Wiley & Sons, New Jersey.
22 Swarm Intelligence and Bio-Inspired Computation